Spatially varying auto-regressive models for prediction of new human immunodeficiency virus diagnoses.

ABSTRACT: In demand of predicting new HIV diagnosis rates based on publicly available HIV data that is abundant in space but has few points in time, we propose a class of spatially varying autoregressive (SVAR) models compounded with conditional autoregressive (CAR) spatial correlation structures. We then propose to use the copula approach and a flexible CAR formulation to model the dependence between adjacent counties. These models allow for spatial and temporal correlation as well as space-time interactions and are naturally suitable for predicting HIV cases and other spatio-temporal disease data that feature a similar data structure. We apply the proposed models to HIV data over Florida, California and New England states and compare them to a range of linear mixed models that have been recently p

SUBMITTER: Shand L 

PROVIDER: S-EPMC6404983 | biostudies-literature | 2018 Aug

REPOSITORIES: biostudies-literature